This research proposes a Mandani-type fuzzy controller to manage autonomous rover navigation. Fuzzy logic systems have proven effective in dealing with the inherent uncertainties in real-world applications, making them particularly suitable for tasks such as rover navigation. The study addresses designing and implementing a fuzzy control system tailored to rover navigation scenarios’ dynamic and unpredictable nature. The Mandani fuzzy controller employs linguistic rules to model intricate relationships among input variables, minimizing errors in wheel speed, steer mechanism, and 6-DOF robot arm trajectories. The implementation is validated through Matlab/Simulink TM simulation studies, using representative scenarios to evaluate the controller’s performance in demanding environments. Evaluation metrics include trajectory accuracy, obstacle avoidance, and overall system robustness. The results of this research are intended to provide valuable insights into the application of fuzzy control systems for autonomous rover navigation, with implications for improving the reliability and adaptability of robotic exploration missions.
WongCYangEYanXT, et al. An overview of robotics and autonomous systems for harsh environments. In: 2017 23rd international conference on automation and computing (ICAC), Huddersfield, UK, 2017, pp.1–6. IEEE.
2.
NesnasIADFesqLMVolpeRA.Autonomy for space robots: past, present, and future. Curr Robot Rep2021; 2(3): 251–263.
3.
LiuB.Recent advancements in autonomous robots and their technical analysis. Math Probl Eng2021; 2021: 1–12.
4.
MillerDPAtkinsonDJWilcoxBH, et al. Autonomous navigation and control of a mars rover. In: NishimuraT (ed.) Automatic control in aerospace 1989. Amsterdam: Elsevier, 1990, pp.111–114.
5.
LacroixSMalletABonnafousD, et al. Autonomous rover navigation on unknown terrains. Int J Robotic Res2002; 21(10): 917–942.
6.
VolpeREstlinTLaubachS, et al. Enhanced mars rover navigation techniques. In: Proceedings 2000 ICRA. Millennium conference. IEEE international conference on robotics and automation. Symposia proceedings, San Francisco, CA, USA, 2000, pp.926–931. IEEE.
HassanNSaleemA.Neural network based adaptive controller for trajectory tracking of wheeled mobile robots. IEEE Access2022; 10: 13582–13591.
9.
HelmickDMRoumeliotisSIChengY, et al. Slip-compensated path following for planetary exploration rovers. Adv Robot2006; 20(11): 1257–1280.
10.
GerdesLAzkarateMSánchez-IbáñezJR, et al. Efficient autonomous navigation for planetary rovers with limited resources. J Field Robot2020; 37(7): 1153–1170.
11.
OgataK.Modern control engineering. Hoboken, NJ: Prentice Hall, 2010.
12.
Olivares-MendezMMadrigalJ. Fuzzy logic user adaptive navigation control system for mobile robots in unknown environments. In: Proceedings of the IEEE international workshop on intelligent signal processing, 2007.
13.
ZhuAYangS. An adaptive neuro-fuzzy controller for robot navigation. In: YuW (ed.) Recent advances in intelligent control systems. London: Springer, 2009, pp.277–307.
14.
NguyenHSugenoM.Fuzzy systems: modeling and control. New York, USA: Springer Science & Business Media, 2012.
ZadehGoguen JA. L. a.Fuzzy sets. Information and control, vol. 8 (1965), pp. 338–353. - l. a. Zadeh. Similarity relations and fuzzy orderings. Information sciences, vol. 3 (1971), pp. 177–200. J Symb Logic1973; 38(4): 656–657.
17.
NorRKhairunizamWSaidonM, et al. Design membership functions of a fuzzy logic controller based on experimental study for an obstacle avoidance algorithm. 2013.
18.
Ramírez-CortésJMGómez-GilPMartínez-CarballidoJ, et al. A labview-based autonomous vehicle navigation system using robot vision and fuzzy control. Ingeniería, investigación y tecnología2011; 12(2): 129–136.
19.
HoffmannF.Evolutionary algorithms for fuzzy control system design. Proc IEEE2001; 89(9): 1318–1333.
20.
BerenjiHR.A reinforcement learning—based architecture for fuzzy logic control. Int J Approx Reason1992; 6(2): 267–292.
21.
BaiYWangD.Fundamentals of fuzzy logic control — fuzzy sets, fuzzy rules and defuzzifications. In: BaiYZhuangHWangD (eds) Advanced fuzzy logic technologies in industrial applications. London: Springer, 2006, pp.17–36.
22.
IsermannR.On fuzzy logic applications for automatic control, supervision, and fault diagnosis. IEEE Trans Syst Man Cybern A Syst Hum1998; 28(2): 221–235.
23.
PedryczW.Principles and methodology of fuzzy sets. J Intell Manuf1993; 4(5): 323–340.
24.
MuñozPBelluttaPR-MorenoMD.Proposing new path-planning metrics for operating rovers on mars. Sci Rep2023; 13(1): 22256.
25.
Van CuongPNanWY. Adaptive trajectory tracking neural network control with robust compensator for robot manipulators. Neural Comput Appl2015; 27(2): 525–536.
26.
ShitsukaneAOtienoC. Effects of membership functions for fuzzy logic controlled autonomous mobile robot. In: Proceedings of the international conference on robotics and automation (ICRA), 2020, pp.1–7.
27.
FilipJAzkarateMVisentinG. Trajectory control for autonomous planetary rovers. In: 14th symposium on advanced space technologies in robotics and automation (ASTRA), 2017.
28.
SniderJM. Automatic steering methods for autonomous automobile path tracking. Robotics Institute, Pittsburgh, PA, Tech. Rep. CMU-RITR-09-08, 2009.
29.
Aranda-EscolasticoEGuinaldoMHeradioR, et al. Event-based control: a bibliometric analysis of twenty years of research. IEEE Access2020; 8: 47188–47208.
KrebsAThueerTCarrascoEVM, et al. Towards torque control of the crab rover. 2008.
33.
GonzalezRApostolopoulosDIagnemmaK.Improving rover mobility through traction control: simulating rovers on the moon. Auton Robots2019; 43(8): 1977–1988.
34.
Hidalgo-CarrióJPoulakisPKirchnerF.Adaptive localization and mapping with application to planetary rovers. J Field Robot2018; 35(6): 961–987.
35.
PeynotTLuiSMcAllisterR, et al. Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain. J Field Robot2014; 31(6): 969–995.
36.
BoutonAGaoY.Marcel: mobile active rover chassis for enhanced locomotion. J Field Robot2023; 40(6): 1504–1524.
